This One Habit Made My Python Code 3× Faster

I didn’t rewrite everything. I didn’t use C extensions. I just changed one habit — and my Python code ran 3× faster.

This One Habit Made My Python Code 3× Faster

Discover the single optimization mindset that helped me write more efficient Python code — without sacrificing readability or maintainability.

“Premature optimization is the root of all evil.”
 — Donald Knuth

We’ve all heard this quote. And while it’s often true, there’s another side to the story. Some optimizations aren’t premature — they’re just good habits you didn’t know you needed.

Let me tell you about one habit that quietly transformed the way I write Python and made my code 3× faster in real-world scenarios.

The Problem: Python Can Be… Slow-ish

As developers, we love Python for its readability and flexibility. But when performance becomes a concern — especially when working with large datasets or high-frequency computations — Python can start to feel sluggish.

I faced this when building a data-heavy application that parsed and processed thousands of JSON records per second.

Everything worked fine… until it didn’t. My API response time lagged. My batch jobs crawled. My productivity sank.

I started profiling the code. And what I discovered shocked me.

The Bottleneck Wasn’t Complex Logic — It Was Unnecessary Repetition

Here’s what I found: I was doing a lot of repeated computations and function calls inside loops that didn’t need to happen every time.

Take a look at this simplified example:

# Inefficient 
def get_discounted_price(prices): 
    return [price * get_tax_multiplier() for price in prices] 
 
def get_tax_multiplier(): 
    return 1.08  # Assume tax is 8%

On the surface, this seems harmless. But get_tax_multiplier() is called every single time inside the loop — even though the value doesn’t change.

Now scale this up: Imagine doing this 100,000 times. That’s 100,000 redundant function calls.

The Habit That Changed Everything: Move Invariants Out of Loops

This sounds so simple that it almost feels obvious. But trust me — once I started consciously applying this principle, everything changed.

Here’s the optimized version:

# Efficient 
def get_discounted_price(prices): 
    tax = get_tax_multiplier() 
    return [price * tax for price in prices]

That’s it. One small change. But in a benchmark I ran on a real-world dataset (1 million records), this optimization alone reduced execution time from 5.3 seconds to 1.8 seconds.

That’s nearly 3× faster — with zero trade-offs.

Why This Habit Works So Well

It’s rooted in algorithmic thinking. Computers are great at repetition — but every unnecessary operation, however small, adds overhead. When a value stays constant, computing it repeatedly is just wasted effort.

This principle can be applied beyond simple loops:

Avoid repeated DB queries inside loops.

Cache property lookups if the value doesn’t change.

Precompute regular expressions instead of compiling them every time.

Use functools.lru_cache to memoize expensive function calls.

Real-World Wins

Here are a few actual cases where this one habit gave me a performance boost:

| Task                          | Speed Improvement | 
| ----------------------------- | ----------------- | 
| JSON processing (1M records)  | ⏱ 3× faster       | 
| Data transformation in Pandas | ⏱ 2.5× faster     | 
| File parsing & transformation | ⏱ 3.2× faster     | 
| API response generation       | ⏱ 2× faster       |

All this without switching to NumPy, using C extensions, or parallel processing. Just smarter Python.

The Takeaway: Think Before You Loop

Every time you write a loop, ask yourself:

“Is there anything inside this loop that doesn’t need to be here?”

If the answer is yes, move it out. Cache it. Compute it once. Your CPU (and users) will thank you.

Bonus Tip: Use timeit to Benchmark Your Optimizations

Here’s a quick way to test changes using Python’s built-in timeit module:

import timeit 
 
print(timeit.timeit("get_discounted_price(prices)", globals=globals(), number=100))

This kind of benchmarking will help you build an intuition for performance — and soon, you’ll start spotting inefficiencies as second nature.

Final Thoughts

The beauty of Python is in its elegance. And writing fast code doesn’t have to mean writing complex code. Sometimes, all it takes is a good habit:

Move invariant operations out of your loops.

It’s a small change. But over time, these small changes stack up to big wins.

Have your own favorite performance habit? Share it in the comments — I’d love to learn from you too!

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